Overview

Dataset statistics

Number of variables28
Number of observations10000
Missing cells60001
Missing cells (%)21.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory224.0 B

Variable types

Numeric11
Categorical11
Unsupported6

Alerts

TermlySessionsUnauthorised has constant value "0"Constant
T_Reason_F has constant value "0"Constant
UPN has a high cardinality: 8411 distinct valuesHigh cardinality
EntryDate has a high cardinality: 382 distinct valuesHigh cardinality
EnrolStatus is highly imbalanced (64.9%)Imbalance
T_Reason_R is highly imbalanced (97.4%)Imbalance
T_Reason_U is highly imbalanced (50.9%)Imbalance
T_Reason_N is highly imbalanced (98.7%)Imbalance
Surname has 10000 (100.0%) missing valuesMissing
Forename has 10000 (100.0%) missing valuesMissing
Middlenames has 10000 (100.0%) missing valuesMissing
PreferredSurname has 10000 (100.0%) missing valuesMissing
FormerSurname has 10000 (100.0%) missing valuesMissing
DoB has 10000 (100.0%) missing valuesMissing
UPN is uniformly distributedUniform
Surname is an unsupported type, check if it needs cleaning or further analysisUnsupported
Forename is an unsupported type, check if it needs cleaning or further analysisUnsupported
Middlenames is an unsupported type, check if it needs cleaning or further analysisUnsupported
PreferredSurname is an unsupported type, check if it needs cleaning or further analysisUnsupported
FormerSurname is an unsupported type, check if it needs cleaning or further analysisUnsupported
DoB is an unsupported type, check if it needs cleaning or further analysisUnsupported
TermlySessionsAuthorised has 934 (9.3%) zerosZeros
T_Reason_I has 664 (6.6%) zerosZeros
T_Reason_M has 3216 (32.2%) zerosZeros
T_Reason_S has 188 (1.9%) zerosZeros
T_Reason_T has 3156 (31.6%) zerosZeros
T_Reason_E has 2521 (25.2%) zerosZeros
T_Reason_C has 1140 (11.4%) zerosZeros
T_Reason_G has 2271 (22.7%) zerosZeros
T_Reason_O has 903 (9.0%) zerosZeros

Reproduction

Analysis started2023-06-26 14:04:36.475053
Analysis finished2023-06-26 14:05:07.029442
Duration30.55 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Estab
Real number (ℝ)

Distinct9609
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189960.59
Minimum59255
Maximum295130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:07.135947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum59255
5-th percentile130874.25
Q1165772.75
median190451
Q3214346.75
95-th percentile249003.85
Maximum295130
Range235875
Interquartile range (IQR)48574

Descriptive statistics

Standard deviation35888.859
Coefficient of variation (CV)0.18892792
Kurtosis-0.10553818
Mean189960.59
Median Absolute Deviation (MAD)24296.5
Skewness-0.053451025
Sum1.8996059 × 109
Variance1.2880102 × 109
MonotonicityNot monotonic
2023-06-26T15:05:07.434320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
196403 3
 
< 0.1%
185136 3
 
< 0.1%
195874 3
 
< 0.1%
223141 3
 
< 0.1%
197646 3
 
< 0.1%
241923 3
 
< 0.1%
182981 3
 
< 0.1%
220322 3
 
< 0.1%
202535 3
 
< 0.1%
179860 3
 
< 0.1%
Other values (9599) 9970
99.7%
ValueCountFrequency (%)
59255 1
< 0.1%
60023 1
< 0.1%
65065 1
< 0.1%
66018 1
< 0.1%
66274 1
< 0.1%
66652 1
< 0.1%
71969 1
< 0.1%
72097 1
< 0.1%
72179 1
< 0.1%
73498 1
< 0.1%
ValueCountFrequency (%)
295130 1
< 0.1%
292924 1
< 0.1%
292794 1
< 0.1%
292368 1
< 0.1%
292049 1
< 0.1%
291048 1
< 0.1%
290467 1
< 0.1%
290173 1
< 0.1%
289822 1
< 0.1%
289643 1
< 0.1%

UPN
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct8411
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
e4d965f2-1068-40a5-8b8e-e780a31b1613
 
4
96cfa297-45f3-4edb-823e-4ae0000ebe3f
 
4
9122e969-556c-450e-aeb0-4bb0ac619833
 
4
240382aa-9569-4777-a973-1402c7870077
 
4
44b0732c-9022-4557-a9f1-e4a14842b037
 
4
Other values (8406)
9980 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters360000
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6999 ?
Unique (%)70.0%

Sample

1st row0d5a3cb8-0d68-41a2-bb39-f1dc6b2b881e
2nd row0d6334c7-f3b9-4043-8f61-c4a7822a0e9c
3rd row54f771be-a769-4654-973a-b659fcd512a3
4th row446764d2-b2ab-4508-853e-cbc1c571f48a
5th rowea064070-803c-4580-b1a6-3f2bee1f4e40

Common Values

ValueCountFrequency (%)
e4d965f2-1068-40a5-8b8e-e780a31b1613 4
 
< 0.1%
96cfa297-45f3-4edb-823e-4ae0000ebe3f 4
 
< 0.1%
9122e969-556c-450e-aeb0-4bb0ac619833 4
 
< 0.1%
240382aa-9569-4777-a973-1402c7870077 4
 
< 0.1%
44b0732c-9022-4557-a9f1-e4a14842b037 4
 
< 0.1%
e3640f88-485b-4f89-ba8a-a25ac4aaa29a 4
 
< 0.1%
908652f9-5356-4ce6-9362-8d4f8bb47aa4 4
 
< 0.1%
3f61de0d-7306-40f6-ba08-a5c3e86c8c59 4
 
< 0.1%
182edd42-fd56-46f9-943f-721f253088b6 4
 
< 0.1%
10d1f7c4-0849-4d4d-945b-567cc1734b90 4
 
< 0.1%
Other values (8401) 9960
99.6%

Length

2023-06-26T15:05:07.690906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e4d965f2-1068-40a5-8b8e-e780a31b1613 4
 
< 0.1%
3f61de0d-7306-40f6-ba08-a5c3e86c8c59 4
 
< 0.1%
96cfa297-45f3-4edb-823e-4ae0000ebe3f 4
 
< 0.1%
9d7773ee-081b-4540-af54-07c6d8de750b 4
 
< 0.1%
10d1f7c4-0849-4d4d-945b-567cc1734b90 4
 
< 0.1%
182edd42-fd56-46f9-943f-721f253088b6 4
 
< 0.1%
b1d5fd8c-227d-47b3-8353-f50a391bc9d4 4
 
< 0.1%
908652f9-5356-4ce6-9362-8d4f8bb47aa4 4
 
< 0.1%
e3640f88-485b-4f89-ba8a-a25ac4aaa29a 4
 
< 0.1%
44b0732c-9022-4557-a9f1-e4a14842b037 4
 
< 0.1%
Other values (8401) 9960
99.6%

Most occurring characters

ValueCountFrequency (%)
- 40000
 
11.1%
4 28840
 
8.0%
b 21388
 
5.9%
a 21355
 
5.9%
9 21274
 
5.9%
8 20966
 
5.8%
3 18926
 
5.3%
7 18924
 
5.3%
1 18879
 
5.2%
0 18844
 
5.2%
Other values (7) 130604
36.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202620
56.3%
Lowercase Letter 117380
32.6%
Dash Punctuation 40000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 28840
14.2%
9 21274
10.5%
8 20966
10.3%
3 18926
9.3%
7 18924
9.3%
1 18879
9.3%
0 18844
9.3%
5 18774
9.3%
2 18684
9.2%
6 18509
9.1%
Lowercase Letter
ValueCountFrequency (%)
b 21388
18.2%
a 21355
18.2%
d 18754
16.0%
c 18745
16.0%
f 18640
15.9%
e 18498
15.8%
Dash Punctuation
ValueCountFrequency (%)
- 40000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 242620
67.4%
Latin 117380
32.6%

Most frequent character per script

Common
ValueCountFrequency (%)
- 40000
16.5%
4 28840
11.9%
9 21274
8.8%
8 20966
8.6%
3 18926
7.8%
7 18924
7.8%
1 18879
7.8%
0 18844
7.8%
5 18774
7.7%
2 18684
7.7%
Latin
ValueCountFrequency (%)
b 21388
18.2%
a 21355
18.2%
d 18754
16.0%
c 18745
16.0%
f 18640
15.9%
e 18498
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 40000
 
11.1%
4 28840
 
8.0%
b 21388
 
5.9%
a 21355
 
5.9%
9 21274
 
5.9%
8 20966
 
5.8%
3 18926
 
5.3%
7 18924
 
5.3%
1 18879
 
5.2%
0 18844
 
5.2%
Other values (7) 130604
36.3%

Surname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Forename
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Middlenames
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

PreferredSurname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

FormerSurname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
M
5157 
F
4843 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M 5157
51.6%
F 4843
48.4%

Length

2023-06-26T15:05:07.873797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:05:08.035435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
m 5157
51.6%
f 4843
48.4%

Most occurring characters

ValueCountFrequency (%)
M 5157
51.6%
F 4843
48.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 5157
51.6%
F 4843
48.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 10000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 5157
51.6%
F 4843
48.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 5157
51.6%
F 4843
48.4%

DoB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

EnrolStatus
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
C
7954 
Leaver
1908 
M
 
111
S
 
22
F
 
5

Length

Max length6
Median length1
Mean length1.954
Min length1

Characters and Unicode

Total characters19540
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 7954
79.5%
Leaver 1908
 
19.1%
M 111
 
1.1%
S 22
 
0.2%
F 5
 
0.1%

Length

2023-06-26T15:05:08.244256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:05:08.457238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
c 7954
79.5%
leaver 1908
 
19.1%
m 111
 
1.1%
s 22
 
0.2%
f 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
C 7954
40.7%
e 3816
19.5%
L 1908
 
9.8%
a 1908
 
9.8%
v 1908
 
9.8%
r 1908
 
9.8%
M 111
 
0.6%
S 22
 
0.1%
F 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
51.2%
Lowercase Letter 9540
48.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 7954
79.5%
L 1908
 
19.1%
M 111
 
1.1%
S 22
 
0.2%
F 5
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 3816
40.0%
a 1908
20.0%
v 1908
20.0%
r 1908
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19540
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 7954
40.7%
e 3816
19.5%
L 1908
 
9.8%
a 1908
 
9.8%
v 1908
 
9.8%
r 1908
 
9.8%
M 111
 
0.6%
S 22
 
0.1%
F 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 7954
40.7%
e 3816
19.5%
L 1908
 
9.8%
a 1908
 
9.8%
v 1908
 
9.8%
r 1908
 
9.8%
M 111
 
0.6%
S 22
 
0.1%
F 5
 
< 0.1%

EntryDate
Categorical

Distinct382
Distinct (%)3.8%
Missing1
Missing (%)< 0.1%
Memory size78.2 KiB
2009-09-03 12:00:00
955 
2010-09-02 12:00:00
910 
2011-09-07 12:00:00
766 
2013-09-05 12:00:00
751 
2011-09-05 12:00:00
697 
Other values (377)
5920 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters189981
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique169 ?
Unique (%)1.7%

Sample

1st row2012-09-04 12:00:00
2nd row2011-09-05 12:00:00
3rd row2012-09-01 12:00:00
4th row2010-09-02 12:00:00
5th row2013-09-05 12:00:00

Common Values

ValueCountFrequency (%)
2009-09-03 12:00:00 955
 
9.6%
2010-09-02 12:00:00 910
 
9.1%
2011-09-07 12:00:00 766
 
7.7%
2013-09-05 12:00:00 751
 
7.5%
2011-09-05 12:00:00 697
 
7.0%
2012-09-06 12:00:00 686
 
6.9%
2010-09-01 12:00:00 612
 
6.1%
2012-09-04 12:00:00 590
 
5.9%
2013-09-03 12:00:00 525
 
5.2%
2009-09-01 12:00:00 419
 
4.2%
Other values (372) 3088
30.9%

Length

2023-06-26T15:05:08.686375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12:00:00 9999
50.0%
2009-09-03 955
 
4.8%
2010-09-02 910
 
4.6%
2011-09-07 766
 
3.8%
2013-09-05 751
 
3.8%
2011-09-05 697
 
3.5%
2012-09-06 686
 
3.4%
2010-09-01 612
 
3.1%
2012-09-04 590
 
3.0%
2013-09-03 525
 
2.6%
Other values (373) 3507
 
17.5%

Most occurring characters

ValueCountFrequency (%)
0 72733
38.3%
2 23675
 
12.5%
1 22962
 
12.1%
- 19998
 
10.5%
: 19998
 
10.5%
9 10874
 
5.7%
9999
 
5.3%
3 4203
 
2.2%
5 1892
 
1.0%
4 1433
 
0.8%
Other values (3) 2214
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 139986
73.7%
Dash Punctuation 19998
 
10.5%
Other Punctuation 19998
 
10.5%
Space Separator 9999
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 72733
52.0%
2 23675
 
16.9%
1 22962
 
16.4%
9 10874
 
7.8%
3 4203
 
3.0%
5 1892
 
1.4%
4 1433
 
1.0%
6 1244
 
0.9%
7 902
 
0.6%
8 68
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 19998
100.0%
Other Punctuation
ValueCountFrequency (%)
: 19998
100.0%
Space Separator
ValueCountFrequency (%)
9999
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 189981
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 72733
38.3%
2 23675
 
12.5%
1 22962
 
12.1%
- 19998
 
10.5%
: 19998
 
10.5%
9 10874
 
5.7%
9999
 
5.3%
3 4203
 
2.2%
5 1892
 
1.0%
4 1433
 
0.8%
Other values (3) 2214
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 72733
38.3%
2 23675
 
12.5%
1 22962
 
12.1%
- 19998
 
10.5%
: 19998
 
10.5%
9 10874
 
5.7%
9999
 
5.3%
3 4203
 
2.2%
5 1892
 
1.0%
4 1433
 
0.8%
Other values (3) 2214
 
1.2%

NCyearActual
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
10
2062 
9
1976 
11
1821 
8
1763 
12
1228 
Other values (4)
1150 

Length

Max length19
Median length2
Mean length1.883
Min length1

Characters and Unicode

Total characters18830
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row8
3rd row9
4th row12
5th row8

Common Values

ValueCountFrequency (%)
10 2062
20.6%
9 1976
19.8%
11 1821
18.2%
8 1763
17.6%
12 1228
12.3%
Leaver 731
 
7.3%
7 406
 
4.1%
13 10
 
0.1%
1900-01-08 00:00:00 3
 
< 0.1%

Length

2023-06-26T15:05:08.947572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:05:09.211416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
10 2062
20.6%
9 1976
19.8%
11 1821
18.2%
8 1763
17.6%
12 1228
12.3%
leaver 731
 
7.3%
7 406
 
4.1%
13 10
 
0.1%
1900-01-08 3
 
< 0.1%
00:00:00 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 6948
36.9%
0 2092
 
11.1%
9 1979
 
10.5%
8 1766
 
9.4%
e 1462
 
7.8%
2 1228
 
6.5%
L 731
 
3.9%
a 731
 
3.9%
v 731
 
3.9%
r 731
 
3.9%
Other values (5) 431
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14429
76.6%
Lowercase Letter 3655
 
19.4%
Uppercase Letter 731
 
3.9%
Dash Punctuation 6
 
< 0.1%
Other Punctuation 6
 
< 0.1%
Space Separator 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6948
48.2%
0 2092
 
14.5%
9 1979
 
13.7%
8 1766
 
12.2%
2 1228
 
8.5%
7 406
 
2.8%
3 10
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 1462
40.0%
a 731
20.0%
v 731
20.0%
r 731
20.0%
Uppercase Letter
ValueCountFrequency (%)
L 731
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Other Punctuation
ValueCountFrequency (%)
: 6
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14444
76.7%
Latin 4386
 
23.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6948
48.1%
0 2092
 
14.5%
9 1979
 
13.7%
8 1766
 
12.2%
2 1228
 
8.5%
7 406
 
2.8%
3 10
 
0.1%
- 6
 
< 0.1%
: 6
 
< 0.1%
3
 
< 0.1%
Latin
ValueCountFrequency (%)
e 1462
33.3%
L 731
16.7%
a 731
16.7%
v 731
16.7%
r 731
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6948
36.9%
0 2092
 
11.1%
9 1979
 
10.5%
8 1766
 
9.4%
e 1462
 
7.8%
2 1228
 
6.5%
L 731
 
3.9%
a 731
 
3.9%
v 731
 
3.9%
r 731
 
3.9%
Other values (5) 431
 
2.3%

TermlySessionsPossible
Real number (ℝ)

Distinct107
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.1556
Minimum20
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:09.597196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile74
Q198
median113
Q3124
95-th percentile132
Maximum134
Range114
Interquartile range (IQR)26

Descriptive statistics

Standard deviation18.542998
Coefficient of variation (CV)0.16987674
Kurtosis0.74282904
Mean109.1556
Median Absolute Deviation (MAD)12
Skewness-0.95226215
Sum1091556
Variance343.84277
MonotonicityNot monotonic
2023-06-26T15:05:09.926433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129 277
 
2.8%
122 261
 
2.6%
132 261
 
2.6%
123 258
 
2.6%
133 253
 
2.5%
127 251
 
2.5%
130 251
 
2.5%
121 248
 
2.5%
119 242
 
2.4%
128 242
 
2.4%
Other values (97) 7456
74.6%
ValueCountFrequency (%)
20 1
 
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
25 1
 
< 0.1%
28 2
< 0.1%
31 2
< 0.1%
34 1
 
< 0.1%
35 2
< 0.1%
36 3
< 0.1%
37 3
< 0.1%
ValueCountFrequency (%)
134 141
1.4%
133 253
2.5%
132 261
2.6%
131 221
2.2%
130 251
2.5%
129 277
2.8%
128 242
2.4%
127 251
2.5%
126 230
2.3%
125 224
2.2%

TermlySessionsAuthorised
Real number (ℝ)

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3977
Minimum0
Maximum16
Zeros934
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:10.156602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile8
Maximum16
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6219882
Coefficient of variation (CV)0.77169503
Kurtosis0.93598703
Mean3.3977
Median Absolute Deviation (MAD)2
Skewness0.99550352
Sum33977
Variance6.8748222
MonotonicityNot monotonic
2023-06-26T15:05:10.367947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 1849
18.5%
2 1637
16.4%
3 1485
14.8%
4 1189
11.9%
0 934
9.3%
5 931
9.3%
6 704
 
7.0%
7 478
 
4.8%
8 305
 
3.0%
9 212
 
2.1%
Other values (7) 276
 
2.8%
ValueCountFrequency (%)
0 934
9.3%
1 1849
18.5%
2 1637
16.4%
3 1485
14.8%
4 1189
11.9%
5 931
9.3%
6 704
 
7.0%
7 478
 
4.8%
8 305
 
3.0%
9 212
 
2.1%
ValueCountFrequency (%)
16 3
 
< 0.1%
15 5
 
0.1%
14 13
 
0.1%
13 26
 
0.3%
12 27
 
0.3%
11 83
 
0.8%
10 119
 
1.2%
9 212
2.1%
8 305
3.0%
7 478
4.8%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-06-26T15:05:10.534739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:05:10.822998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 10000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10000
100.0%

T_Reason_I
Real number (ℝ)

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7486
Minimum0
Maximum22
Zeros664
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:10.966264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile12
Maximum22
Range22
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6114128
Coefficient of variation (CV)0.76052158
Kurtosis0.78902123
Mean4.7486
Median Absolute Deviation (MAD)2
Skewness0.97323682
Sum47486
Variance13.042302
MonotonicityNot monotonic
2023-06-26T15:05:11.142877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 1318
13.2%
2 1265
12.7%
3 1180
11.8%
4 1091
10.9%
5 940
9.4%
6 785
7.8%
7 704
7.0%
0 664
6.6%
8 502
 
5.0%
9 436
 
4.4%
Other values (13) 1115
11.2%
ValueCountFrequency (%)
0 664
6.6%
1 1318
13.2%
2 1265
12.7%
3 1180
11.8%
4 1091
10.9%
5 940
9.4%
6 785
7.8%
7 704
7.0%
8 502
 
5.0%
9 436
 
4.4%
ValueCountFrequency (%)
22 4
 
< 0.1%
21 1
 
< 0.1%
20 5
 
0.1%
19 8
 
0.1%
18 13
 
0.1%
17 26
 
0.3%
16 32
 
0.3%
15 52
0.5%
14 102
1.0%
13 127
1.3%

T_Reason_M
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9294
Minimum0
Maximum5
Zeros3216
Zeros (%)32.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:11.313039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81035875
Coefficient of variation (CV)0.87191602
Kurtosis0.37267279
Mean0.9294
Median Absolute Deviation (MAD)1
Skewness0.68314176
Sum9294
Variance0.65668131
MonotonicityNot monotonic
2023-06-26T15:05:11.495413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 4677
46.8%
0 3216
32.2%
2 1742
 
17.4%
3 331
 
3.3%
4 30
 
0.3%
5 4
 
< 0.1%
ValueCountFrequency (%)
0 3216
32.2%
1 4677
46.8%
2 1742
 
17.4%
3 331
 
3.3%
4 30
 
0.3%
5 4
 
< 0.1%
ValueCountFrequency (%)
5 4
 
< 0.1%
4 30
 
0.3%
3 331
 
3.3%
2 1742
 
17.4%
1 4677
46.8%
0 3216
32.2%

T_Reason_R
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
9974 
1
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9974
99.7%
1 26
 
0.3%

Length

2023-06-26T15:05:11.655558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:05:11.838103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9974
99.7%
1 26
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 9974
99.7%
1 26
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9974
99.7%
1 26
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9974
99.7%
1 26
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9974
99.7%
1 26
 
0.3%

T_Reason_S
Real number (ℝ)

Distinct71
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.8286
Minimum0
Maximum76
Zeros188
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:12.005849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median13
Q323
95-th percentile39
Maximum76
Range76
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.922224
Coefficient of variation (CV)0.75320777
Kurtosis0.67533513
Mean15.8286
Median Absolute Deviation (MAD)8
Skewness0.95260787
Sum158286
Variance142.13944
MonotonicityNot monotonic
2023-06-26T15:05:12.238074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 433
 
4.3%
2 415
 
4.2%
4 394
 
3.9%
5 385
 
3.9%
3 382
 
3.8%
6 380
 
3.8%
7 379
 
3.8%
8 367
 
3.7%
11 352
 
3.5%
12 350
 
3.5%
Other values (61) 6163
61.6%
ValueCountFrequency (%)
0 188
1.9%
1 433
4.3%
2 415
4.2%
3 382
3.8%
4 394
3.9%
5 385
3.9%
6 380
3.8%
7 379
3.8%
8 367
3.7%
9 335
3.4%
ValueCountFrequency (%)
76 1
 
< 0.1%
73 1
 
< 0.1%
68 1
 
< 0.1%
67 2
< 0.1%
66 2
< 0.1%
65 1
 
< 0.1%
64 4
< 0.1%
63 1
 
< 0.1%
62 1
 
< 0.1%
61 4
< 0.1%

T_Reason_T
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9381
Minimum0
Maximum5
Zeros3156
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:12.415703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.80965051
Coefficient of variation (CV)0.86307484
Kurtosis0.3685454
Mean0.9381
Median Absolute Deviation (MAD)1
Skewness0.67520089
Sum9381
Variance0.65553394
MonotonicityNot monotonic
2023-06-26T15:05:12.574620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 4712
47.1%
0 3156
31.6%
2 1770
 
17.7%
3 321
 
3.2%
4 39
 
0.4%
5 2
 
< 0.1%
ValueCountFrequency (%)
0 3156
31.6%
1 4712
47.1%
2 1770
 
17.7%
3 321
 
3.2%
4 39
 
0.4%
5 2
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
4 39
 
0.4%
3 321
 
3.2%
2 1770
 
17.7%
1 4712
47.1%
0 3156
31.6%

T_Reason_H
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
4711 
0
4639 
2
631 
3
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 4711
47.1%
0 4639
46.4%
2 631
 
6.3%
3 19
 
0.2%

Length

2023-06-26T15:05:12.751259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:05:12.888300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4711
47.1%
0 4639
46.4%
2 631
 
6.3%
3 19
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 4711
47.1%
0 4639
46.4%
2 631
 
6.3%
3 19
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4711
47.1%
0 4639
46.4%
2 631
 
6.3%
3 19
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4711
47.1%
0 4639
46.4%
2 631
 
6.3%
3 19
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4711
47.1%
0 4639
46.4%
2 631
 
6.3%
3 19
 
0.2%

T_Reason_F
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-06-26T15:05:13.042778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:05:13.167216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 10000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10000
100.0%

T_Reason_E
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2363
Minimum0
Maximum6
Zeros2521
Zeros (%)25.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:13.275512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0243863
Coefficient of variation (CV)0.82859037
Kurtosis0.56689447
Mean1.2363
Median Absolute Deviation (MAD)1
Skewness0.78173849
Sum12363
Variance1.0493672
MonotonicityNot monotonic
2023-06-26T15:05:13.431384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 4042
40.4%
0 2521
25.2%
2 2303
23.0%
3 878
 
8.8%
4 205
 
2.1%
5 45
 
0.4%
6 6
 
0.1%
ValueCountFrequency (%)
0 2521
25.2%
1 4042
40.4%
2 2303
23.0%
3 878
 
8.8%
4 205
 
2.1%
5 45
 
0.4%
6 6
 
0.1%
ValueCountFrequency (%)
6 6
 
0.1%
5 45
 
0.4%
4 205
 
2.1%
3 878
 
8.8%
2 2303
23.0%
1 4042
40.4%
0 2521
25.2%

T_Reason_C
Real number (ℝ)

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7202
Minimum0
Maximum14
Zeros1140
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:13.587044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile7
Maximum14
Range14
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0995601
Coefficient of variation (CV)0.77184036
Kurtosis1.0040867
Mean2.7202
Median Absolute Deviation (MAD)1
Skewness0.97807413
Sum27202
Variance4.4081528
MonotonicityNot monotonic
2023-06-26T15:05:13.765177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 2269
22.7%
2 1940
19.4%
3 1585
15.8%
4 1189
11.9%
0 1140
11.4%
5 818
 
8.2%
6 491
 
4.9%
7 282
 
2.8%
8 149
 
1.5%
9 72
 
0.7%
Other values (5) 65
 
0.7%
ValueCountFrequency (%)
0 1140
11.4%
1 2269
22.7%
2 1940
19.4%
3 1585
15.8%
4 1189
11.9%
5 818
 
8.2%
6 491
 
4.9%
7 282
 
2.8%
8 149
 
1.5%
9 72
 
0.7%
ValueCountFrequency (%)
14 4
 
< 0.1%
13 1
 
< 0.1%
12 3
 
< 0.1%
11 17
 
0.2%
10 40
 
0.4%
9 72
 
0.7%
8 149
 
1.5%
7 282
 
2.8%
6 491
4.9%
5 818
8.2%

T_Reason_G
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3641
Minimum0
Maximum7
Zeros2271
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:13.930298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1083565
Coefficient of variation (CV)0.81251848
Kurtosis0.64270913
Mean1.3641
Median Absolute Deviation (MAD)1
Skewness0.81072641
Sum13641
Variance1.228454
MonotonicityNot monotonic
2023-06-26T15:05:14.081290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 3857
38.6%
2 2371
23.7%
0 2271
22.7%
3 1071
 
10.7%
4 342
 
3.4%
5 71
 
0.7%
6 13
 
0.1%
7 4
 
< 0.1%
ValueCountFrequency (%)
0 2271
22.7%
1 3857
38.6%
2 2371
23.7%
3 1071
 
10.7%
4 342
 
3.4%
5 71
 
0.7%
6 13
 
0.1%
7 4
 
< 0.1%
ValueCountFrequency (%)
7 4
 
< 0.1%
6 13
 
0.1%
5 71
 
0.7%
4 342
 
3.4%
3 1071
 
10.7%
2 2371
23.7%
1 3857
38.6%
0 2271
22.7%

T_Reason_U
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
7733 
1
2261 
2
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7733
77.3%
1 2261
 
22.6%
2 6
 
0.1%

Length

2023-06-26T15:05:14.248513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:05:14.406891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7733
77.3%
1 2261
 
22.6%
2 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7733
77.3%
1 2261
 
22.6%
2 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7733
77.3%
1 2261
 
22.6%
2 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7733
77.3%
1 2261
 
22.6%
2 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7733
77.3%
1 2261
 
22.6%
2 6
 
0.1%

T_Reason_O
Real number (ℝ)

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6877
Minimum0
Maximum19
Zeros903
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:14.556525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile9
Maximum19
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8574088
Coefficient of variation (CV)0.7748485
Kurtosis0.82988583
Mean3.6877
Median Absolute Deviation (MAD)2
Skewness0.97451674
Sum36877
Variance8.1647852
MonotonicityNot monotonic
2023-06-26T15:05:14.688493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 1695
17.0%
2 1551
15.5%
3 1414
14.1%
4 1110
11.1%
5 936
9.4%
0 903
9.0%
6 764
7.6%
7 533
 
5.3%
8 394
 
3.9%
9 280
 
2.8%
Other values (10) 420
 
4.2%
ValueCountFrequency (%)
0 903
9.0%
1 1695
17.0%
2 1551
15.5%
3 1414
14.1%
4 1110
11.1%
5 936
9.4%
6 764
7.6%
7 533
 
5.3%
8 394
 
3.9%
9 280
 
2.8%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 2
 
< 0.1%
17 1
 
< 0.1%
16 4
 
< 0.1%
15 13
 
0.1%
14 20
 
0.2%
13 33
 
0.3%
12 67
 
0.7%
11 97
1.0%
10 182
1.8%

T_Reason_N
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
9988 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9988
99.9%
1 12
 
0.1%

Length

2023-06-26T15:05:14.840267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:05:15.193222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9988
99.9%
1 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 9988
99.9%
1 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9988
99.9%
1 12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9988
99.9%
1 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9988
99.9%
1 12
 
0.1%

Interactions

2023-06-26T15:05:03.377410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:38.335759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:40.486175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:42.858170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:45.042067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:47.488650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:49.653346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:52.118327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:54.648639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:57.213959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:00.660598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:03.650541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:38.504153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:40.741563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:43.040009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:45.217011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:47.713271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:49.854098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:52.334091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:54.893764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:57.445806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:00.958406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:03.811373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:38.662222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:40.907271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:43.264992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:45.387348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:47.880562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:50.132128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:52.675518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:55.116107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:57.622820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:01.219592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:03.990540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:38.850468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:41.100549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:43.519542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:45.580340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:48.078264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:50.358109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:52.904823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:55.298273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:57.822259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:01.574538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:04.154506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:39.042653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:41.271998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:43.725328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:45.709772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:48.278586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:50.557149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:53.142295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:55.469445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:58.141047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:01.791809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:04.332621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:39.231121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:41.537554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:43.889477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:45.872667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:48.471016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:50.735354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:53.352185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:55.656297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:58.595177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:02.007805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:04.626120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:39.443466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:41.717518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:44.065131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:46.006176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:48.746276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:50.965346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:53.742271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:55.872257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:58.907535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:02.232585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:04.765326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:39.639459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:42.051242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:44.235885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:46.537015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:48.953918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:51.225985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:53.920678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:56.075701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:59.623528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:02.466033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:04.905245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:39.795391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:42.252261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:44.408308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:46.738127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:49.159409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:51.414288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:54.104428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:56.271097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:59.861353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:02.696497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:05.112158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:39.962281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:42.460343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:44.654911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:46.933492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:49.315301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:51.602495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:54.305685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:56.444883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:00.148479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:02.913912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:05.298648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:40.226735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:42.682408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:44.850237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:47.164907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:49.469707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:51.856580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:54.446333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:56.793508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:00.416639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:03.129540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-26T15:05:15.363802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
EstabTermlySessionsPossibleTermlySessionsAuthorisedT_Reason_IT_Reason_MT_Reason_ST_Reason_TT_Reason_ET_Reason_CT_Reason_GT_Reason_OGenderEnrolStatusNCyearActualT_Reason_RT_Reason_HT_Reason_UT_Reason_N
Estab1.000-0.0060.0130.0400.011-0.0780.0200.024-0.0480.009-0.0910.0160.0370.0160.0160.0060.0300.000
TermlySessionsPossible-0.0061.000-0.0420.1790.117-0.312-0.010-0.004-0.0920.0600.0090.0320.1370.1280.0120.0000.0000.000
TermlySessionsAuthorised0.013-0.0421.000-0.146-0.078-0.063-0.0250.004-0.042-0.034-0.0360.0820.0000.0040.0000.0000.0160.000
T_Reason_I0.0400.179-0.1461.0000.149-0.184-0.0060.0290.0140.0210.0990.0200.0230.0580.0360.0310.0410.000
T_Reason_M0.0110.117-0.0780.1491.000-0.102-0.008-0.0000.0280.0060.0380.0490.0320.0410.0000.0110.0380.000
T_Reason_S-0.078-0.312-0.063-0.184-0.1021.000-0.014-0.0590.041-0.071-0.0610.0300.0750.1810.0250.0000.0000.000
T_Reason_T0.020-0.010-0.025-0.006-0.008-0.0141.0000.021-0.006-0.0140.0230.0000.0000.0160.0000.0000.0000.000
T_Reason_E0.024-0.0040.0040.029-0.000-0.0590.0211.0000.055-0.0040.1290.0370.0220.0000.0000.0000.0410.000
T_Reason_C-0.048-0.092-0.0420.0140.0280.041-0.0060.0551.000-0.0140.0780.0000.0320.0110.0000.0090.0180.020
T_Reason_G0.0090.060-0.0340.0210.006-0.071-0.014-0.004-0.0141.0000.0330.0000.0000.0010.0000.0040.0320.000
T_Reason_O-0.0910.009-0.0360.0990.038-0.0610.0230.1290.0780.0331.0000.0330.0240.0000.0000.0000.1020.000
Gender0.0160.0320.0820.0200.0490.0300.0000.0370.0000.0000.0331.0000.0000.0190.0000.0160.0180.000
EnrolStatus0.0370.1370.0000.0230.0320.0750.0000.0220.0320.0000.0240.0001.0000.1010.0230.0000.0000.000
NCyearActual0.0160.1280.0040.0580.0410.1810.0160.0000.0110.0010.0000.0190.1011.0000.0120.0040.0000.000
T_Reason_R0.0160.0120.0000.0360.0000.0250.0000.0000.0000.0000.0000.0000.0230.0121.0000.0000.0000.000
T_Reason_H0.0060.0000.0000.0310.0110.0000.0000.0000.0090.0040.0000.0160.0000.0040.0001.0000.0030.000
T_Reason_U0.0300.0000.0160.0410.0380.0000.0000.0410.0180.0320.1020.0180.0000.0000.0000.0031.0000.000
T_Reason_N0.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-06-26T15:05:05.930275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-26T15:05:06.775778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EstabUPNSurnameForenameMiddlenamesPreferredSurnameFormerSurnameGenderDoBEnrolStatusEntryDateNCyearActualTermlySessionsPossibleTermlySessionsAuthorisedTermlySessionsUnauthorisedT_Reason_IT_Reason_MT_Reason_RT_Reason_ST_Reason_TT_Reason_HT_Reason_FT_Reason_ET_Reason_CT_Reason_GT_Reason_UT_Reason_OT_Reason_N
01933110d5a3cb8-0d68-41a2-bb39-f1dc6b2b881eNaNNaNNaNNaNNaNFNaNC2012-09-04 12:00:0011131404203110020000
11703230d6334c7-f3b9-4043-8f61-c4a7822a0e9cNaNNaNNaNNaNNaNFNaNC2011-09-05 12:00:008924062016010061010
222144354f771be-a769-4654-973a-b659fcd512a3NaNNaNNaNNaNNaNMNaNC2012-09-01 12:00:0091121061022200101060
3248270446764d2-b2ab-4508-853e-cbc1c571f48aNaNNaNNaNNaNNaNFNaNC2010-09-02 12:00:00121090070012100211070
4157448ea064070-803c-4580-b1a6-3f2bee1f4e40NaNNaNNaNNaNNaNMNaNC2013-09-05 12:00:008931042032010212080
51799777a7f914c-2d15-4af6-9a3f-2c378d61aa49NaNNaNNaNNaNNaNFNaNC2011-09-05 12:00:0010726001035010140020
6183957171ad604-ac0f-49fd-89a1-9c41812ae3caNaNNaNNaNNaNNaNMNaNC2010-09-02 12:00:0012796031025100130060
7176675cd367cbc-cdab-43fe-add8-1b114d011607NaNNaNNaNNaNNaNMNaNC2010-09-01 12:00:0081136010028100003000
8134438e1d9156b-da4a-4b69-95a6-604af492cccaNaNNaNNaNNaNNaNFNaNC2013-09-01 12:00:007940001042200141020
9144059f7b2f1b9-6aee-4c90-93d7-2e891250a61cNaNNaNNaNNaNNaNFNaNC2013-09-03 12:00:00121242072023000120120
EstabUPNSurnameForenameMiddlenamesPreferredSurnameFormerSurnameGenderDoBEnrolStatusEntryDateNCyearActualTermlySessionsPossibleTermlySessionsAuthorisedTermlySessionsUnauthorisedT_Reason_IT_Reason_MT_Reason_RT_Reason_ST_Reason_TT_Reason_HT_Reason_FT_Reason_ET_Reason_CT_Reason_GT_Reason_UT_Reason_OT_Reason_N
9990151043d0eb33ca-ecb7-4477-a2c8-65fea944f1c4NaNNaNNaNNaNNaNFNaNC2009-09-04 12:00:0011133103103110091030
9991137912118ee579-1abf-4444-8b88-f963579087bcNaNNaNNaNNaNNaNMNaNC2012-09-06 12:00:0091263021035210130170
9992138009613a6fa3-1e33-479c-92cd-652daa547ee1NaNNaNNaNNaNNaNMNaNC2013-09-03 12:00:00Leaver1182030012000000160
99931879717ea5b452-41da-469b-a67b-98834ccc53e9NaNNaNNaNNaNNaNFNaNC2011-09-01 12:00:008891052014110120020
9994229575dbd3fc70-ce7e-496e-9bd9-db4991e654e9NaNNaNNaNNaNNaNMNaNC2013-09-05 12:00:009130908101210211000
999517977163353ed7-48da-4e50-89d4-25b4fd673a7bNaNNaNNaNNaNNaNFNaNC2013-09-06 12:00:0089011091013100011050
9996154953c029c59d-38c8-4124-a3da-f02e34e0e3d4NaNNaNNaNNaNNaNMNaNC2009-09-01 12:00:009125000001110122180
9997193714ec0fb2cf-193a-45ee-8093-04cb6f1232d2NaNNaNNaNNaNNaNFNaNC2013-09-05 12:00:00111241062011200101010
99981855181ceb3069-9210-4ee4-ab9f-ff056d6fa35dNaNNaNNaNNaNNaNMNaNLeaver2012-09-06 12:00:00101110021010020121010
999918795885d128dc-4d36-4ef9-b6b0-fd53a8072da8NaNNaNNaNNaNNaNFNaNC2013-09-05 12:00:008811041025000211060